Person:
González Briones, Alfonso

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First Name
Alfonso
Last Name
González Briones
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Ingeniería del Software e Inteligencia Artificial
Area
Lenguajes y Sistemas Informáticos
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    Publication
    An Adjective Selection Personality Assessment Method Using Gradient Boosting Machine Learning
    (MDPI, 2020-05-21) Fernandes, Bruno; González Briones, Alfonso; Novais, Paulo; Calafate, Miguel; Analide, Cesar; Neves, José
    Goldberg’s 100 Unipolar Markers remains one of the most popular ways to measure personality traits, in particular, the Big Five. An important reduction was later preformed by Saucier, using a sub-set of 40 markers. Both assessments are performed by presenting a set of markers, or adjectives, to the subject, requesting him to quantify each marker using a 9-point rating scale. Consequently, the goal of this study is to conduct experiments and propose a shorter alternative where the subject is only required to identify which adjectives describe him the most. Hence, a web platform was developed for data collection, requesting subjects to rate each adjective and select those describing him the most. Based on a Gradient Boosting approach, two distinct Machine Learning architectures were conceived, tuned and evaluated. The first makes use of regressors to provide an exact score of the Big Five while the second uses classifiers to provide a binned output. As input, both receive the one-hot encoded selection of adjectives. Both architectures performed well. The first is able to quantify the Big Five with an approximate error of 5 units of measure, while the second shows a micro-averaged f1-score of 83%. Since all adjectives are used to compute all traits, models are able to harness inter-trait relationships, being possible to further reduce the set of adjectives by removing those that have smaller importance.
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    Publication
    Deepint.net: A Rapid Deployment Platform for Smart Territories
    (MPDI, 2021-01-01) Corchado, Juan M.; Chamoso, Pablo; Hernández, Guillermo; Gutierrez, Agustín San Roman; Camacho, Alberto Rivas; González Briones, Alfonso; Pinto Santos, Francisco; Goyenechea, Enrique; Garcia Retuerta, David; Alonso Miguel, María; Hernandez, Beatriz Bellido; Villaverde, Diego Valdeolmillos; Sánchez Verdejo, Manuel; Plaza Martínez, Pablo; López Pérez, Manuel; Manzano García, Sergio; Alonso, Ricardo S.; Casado Vara, Roberto; Tejedor, Javier Prieto; Prieta, Fernando de la; Rodríguez González, Sara; Parra Domínguez, Javier; Mohamad, Mohd Saberi; Trabelsi, Saber; Díaz Plaza, Enrique; Garcia Coria, Jose Alberto; Yigitcanlar, Tan; Novais, Paulo; Omatu, Sigeru
    This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Vélib’ Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.